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In this Azure Databricks tutorial, we explain what is schema in PySpark and why it plays a crucial role in ensuring data consistency and performance. You’ll also learn how to manually define a schema using StructType and StructField, and how to apply it while reading data using spark.read for better control over your DataFrame structure.

Whether you're working with large datasets or just starting out with PySpark, understanding schemas is crucial for managing your data efficiently.

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🔥 Following are the topics we covered in this defining Schema in PySpark video:
✔ What is a schema in PySpark?
✔ Why defining schema is important?
✔ How to define schema manually using StructType and StructField?
✔ Reading data with schema using spark.read

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